Low Spatial Variability Research Articles

Characterizing the distribution pattern of traffic-related air pollutants in near-road neighborhoods.

In near-road neighborhoods, residents are more frequently exposed to traffic-related air pollution (TRAP), and they are increasingly aware of pollution levels. Given this consideration, this study adopted portable air pollutant sensors to conduct a mobile monitoring campaign in two near-road neighborhoods, one in an urban area and one in a suburban area of Shanghai, China. The campaign characterized spatiotemporal distributions of fine particulate matter (PM2.5) and black carbon (BC) to help identify appropriate mitigation measures in these near-road micro-environments. The study identified higher mean TRAP concentrations (up to 4.7-fold and 1.7-fold higher for PM2.5 and BC, respectively), lower spatial variability, and a stronger inter-pollutant correlation in winter compared to summer. The temporal variations of TRAP between peak hour and off-peak hour were also investigated. It was identified that district-level PM2.5 increments occurred from off-peak to peak hours, with BC concentrations attributed more to traffic emissions. In addition, the spatiotemporal distribution of TRAP inside neighborhoods revealed that PM2.5 concentrations presented great temporal variability but almost remained invariant in space, while the BC concentrations showed notable spatiotemporal variability. These findings provide valuable insights into the unique spatiotemporal distributions of TRAP in different near-road neighborhoods, highlighting the important role of hyperlocal monitoring in urban micro-environments to support tailored designing and implementing appropriate mitigation measures.

High parasite prevalence in an ecosystem engineer correlated with both local- and landscape-level factors.

Spatial variation in parasitic infection may have many physical and biological drivers. Uncovering these drivers may be especially important for parasites of ecosystem engineers because the engineers are foundational to their communities. Oysters are an important coastal ecosystem engineer that have declined drastically worldwide, in part due to enhanced cases of lethal oyster diseases, such as Dermo and MSX, caused by the protozoan parasites Perkinsus marinus and Haplosporidium nelsoni, respectively. Besides water quality and hydrodynamics, there is little information on how other variables influence the prevalence and intensity of these pathogens in oysters across a regional scale. To examine drivers of spatial variation in these oyster parasites-including host size, local reef properties, and landscape properties-we sampled 24 reefs systematically spread along the coast of Georgia, USA. Across sites, we found universally high prevalence of oysters with at least one of these parasites (91.02% ± 8.89, mean ± SD). Not only are high levels of parasite prevalence potentially problematic for a pivotal ecosystem engineer, but also low spatial variability may limit the explanatory power of variables across a regional scale. Our statistical models explained between 18 and 42% of the variation in spatial patterns of prevalence and intensity of these microparasites. Interestingly, landscape context was a positive predictor of P. marinus, but a negative predictor of H. nelsoni. Overall, our findings suggest that factors driving parasite prevalence and intensity operate across multiple spatial scales, and the same factor can both facilitate and hinder different parasites within the same host species.

Microstate-based brain network dynamics distinguishing temporal lobe epilepsy patients: A machine learning approach

Temporal lobe epilepsy (TLE) stands as the predominant adult focal epilepsy syndrome, characterized by dysfunctional intrinsic brain dynamics. However, the precise mechanisms underlying seizures in these patients remain elusive. Our study encompassed 116 TLE patients compared with 51 healthy controls. Employing microstate analysis, we assessed brain dynamic disparities between TLE patients and healthy controls, as well as between drug-resistant epilepsy (DRE) and drug-sensitive epilepsy (DSE) patients. We constructed dynamic functional connectivity networks based on microstates and quantified their spatial and temporal variability. Utilizing these brain network features, we developed machine learning models to discriminate between TLE patients and healthy controls, and between DRE and DSE patients. Temporal dynamics in TLE patients exhibited significant acceleration compared to healthy controls, along with heightened synchronization and instability in brain networks. Moreover, DRE patients displayed notably lower spatial variability in certain parts of microstate B, E and F dynamic functional connectivity networks, while temporal variability in certain parts of microstate E and G dynamic functional connectivity networks was markedly higher in DRE patients compared to DSE patients. The machine learning model based on these spatiotemporal metrics effectively differentiated TLE patients from healthy controls and discerned DRE from DSE patients. The accelerated microstate dynamics and disrupted microstate sequences observed in TLE patients mirror highly unstable intrinsic brain dynamics, potentially underlying abnormal discharges. Additionally, the presence of highly synchronized and unstable activities in brain networks of DRE patients signifies the establishment of stable epileptogenic networks, contributing to the poor responsiveness to antiseizure medications. The model based on spatiotemporal metrics demonstrated robust predictive performance, accurately distinguishing both TLE patients from healthy controls and DRE patients from DSE patients.

Evaluating the ability of convolutional neural networks for transfer learning in Pinus radiata cover predictions

The species Pinus radiata is highly invasive in native forests in Chile, drastically affecting the functioning and structure of ecosystems. Hence, it is imperative to develop robust approaches to detect P. radiata invasions at different scales. Models based on convolutional neural networks (CNN) have proven to be a promising alternative to detect plant invasions in high-resolution remote sensing data, such as those obtained by drones. However, studies have been limited in their spatial variability and their assessments of transferability or transfer learning to new sectors, hindering the ability to use these models in a real-world setting. We train models based on CNN architectures using unpiloted aerial vehicle data and evaluate their ability to transfer learning outside the training domain using regression approaches. We compared models trained with low spatial variability (mono-site) with those with high spatial variability (multi-site). We further sought to maximize the transference of learning outside the training domain by searching among different architectures and models, maximizing the evaluation in an independent data set. The results showed that transfer learning is better when multi-site models with higher spatial variability are used for training, obtaining a coefficient of determination R2 between 60% and 87%. On the contrary, mono-site models present a wide variability of performance attributed to the dissimilarity of information between sites, limiting the possibilities of using these models for extrapolations or model generalizations. We also obtained a significant difference between within-domain generalization using test data versus transfer learning outside the training domain, showing that testing data alone cannot depict such discrepancy without further data. Finally, the best models for transfer learning on new data domains often do not agree with those selected by the standard training/validation/testing scheme. Our findings pave the way for deeper discussions and further investigations into the limitations of CNN models when applied to high-resolution imagery.

Spatiotemporal Variability of Hyporheic Flow in a Losing River Section

AbstractCharacterizing the spatiotemporal variability of water fluxes at the stream‐groundwater interface is extremely challenging due to the lack of methods for estimating hyporheic flows at different scales. To address this, we demonstrate the potential of Active‐Distributed Temperature Sensing (DTS) methods for measuring and mapping hyporheic flow in a lowland stream. Experiments were conducted by burying a few hundred meters of heatable Fiber‐Optic cables within streambed sediments in a large meander, where permanent stream‐losing conditions are observed along the stream reach. We propose a new methodology to filter ambient temperature variations along the heated section of the DTS cable and to extend the application of Active‐DTS to losing streams. After data processing, the results show that, along lateral and longitudinal stream profiles, both thermal conductivity and water flux values follow normal distributions with relatively small standard deviations. Hyporheic fluxes vary by one order of magnitude. The absence of correlation between water fluxes within the hyporheic zone and streambed topography variations suggests that the variability is mainly controlled by local streambed heterogeneities. This means that the spatiotemporal variability of fluxes may be used as a marker of the variability of streambed hydraulic conductivities. The relatively low spatial variability (one order of magnitude) in hyporheic flow suggests a small variability of streambed properties. This is an important result for calibrating models assessing hyporheic processes, in which the hydraulic conductivity distribution is generally assumed. Additionally, measurements made over three years yield similar estimates showing the remarkable stability of hyporheic flows through time.

Satellite-based assessment of national carbon monoxide concentrations for air quality reporting in Finland

Carbon monoxide (CO) has negative health effects, especially on the respiratory system, when present in large concentrations in ambient air. Therefore, it is one of the 12 air pollutants with monitoring and assessment obligations set by the European Union Ambient Air Quality Directives. With low CO concentrations in Finland, continuous surface measurements are not compulsory but the monitoring and assessment obligation remains. Currently, there are no air quality stations equipped with CO measurements in Finland, and the assessment can only be based on emission estimates.In this work, we demonstrate the use of satellite-based CO measurements from the TROPOspheric Monitoring Instrument (TROPOMI) on-board Sentinel-5 Precursor satellite (S5P) to strengthen the assessment with actual measurements. Averaging satellite measurements over several years reveals the spatial distribution of CO with nationwide coverage in city-scale resolution. Individual satellite measurements, in turn, are used to estimate the surface concentrations and their annual variability in different regions in Finland.For the surface estimates, we used a simple and robust linear model between the satellite measurements and ground-level in-situ measurements. We found that the linear model produces reasonable surface concentration estimates to be used in air quality assessments where the objective is to evaluate whether the monitoring threshold set by the EU is exceeded on a yearly scale. It is noted that cloudiness can cause local data gaps of several days and the low solar elevation angles prevent satellite measurements from 9 to 18 weeks during the winter time in Finland.In Finland, the average total columns show low spatial variability, with only a few high-concentration areas identified. For the last three years, the estimated surface concentrations have stayed well below the lower assessment threshold set by the Air Quality Directive.

A Comprehensive Assessment of Multiple High-Resolution Precipitation Grid Products for Monitoring Heavy Rainfall during the “7.20” Extreme Rainstorm Event in China

Precipitation products play an important role in monitoring rainstorm processes. This study takes a rare historical event of extreme, heavy precipitation that occurred in Henan Province, China, in July 2021 as a research case. By analyzing the distribution of the spatial and temporal characteristics of precipitation errors, using a probability density function of the occurrence of precipitation and the daily variation pattern, we assess the capability of a radar precipitation estimation product (RADAR), satellite precipitation products (IMERG and GSMAP), a reanalysis product (ERA5) and a precipitation fusion product (the CMPAS) to monitor an extreme rainstorm in the Henan region. The CMPAS has the best fit with the gauge observations in terms of the precipitation area, precipitation maximum and the evolution of the whole process, with a low spatial variability of errors. However, the CMPAS slightly underestimated the precipitation extremum at the peak moment (06:00–08:00). The RADAR product was prone to a spurious overestimation of the originally small rainfall, especially during peak precipitation times, with deviations concentrated in the core precipitation area. The IMERG, GSMAP and ERA5 products have similar performances, all of which failed to effectively capture heavy precipitation in excess of 60 mm/h, with negative deviations in precipitation at mountainfront locations west of northern Henan Province. There is still a need for terrain-specific error revisions for areas with large topographic relief. By merging and processing precipitation data from multiple sources, the accuracy of the CMPAS is better than any single-source precipitation product. The CMPAS has the characteristic advantage of high spatial and temporal resolutions (0.01° × 0.01°/1 h), which play a positive role in precipitation dynamic monitoring, providing early warnings of heavy rainfall processes and hydrological application research.

Delineating Management Zones with Different Yield Potentials in Soybean–Corn and Soybean–Cotton Production Systems

The delineation of management zones is one of the ways to enable the spatially differentiated management of plots using precision agriculture tools. Over the years, the spatial variability of data collected from soil and plant sampling started to be replaced by data collected by proximal and orbital sensors. As a result, the variety and volume of data have increased considerably, making it necessary to use advanced computational tools, such as machine learning, for data analysis and decision-making support. This paper presents a methodology used to establish management zones (MZ) in precision agriculture by analyzing data obtained from soil sampling, proximal sensors and orbital sensors, in experiments carried out in four plots featuring soybean–cotton and soybean–corn crops, in Mato Grosso and Paraná states, Brazil. Four procedures were evaluated, using different input data sets for the MZ delineation: (I) soil attributes, including clay content, apparent electrical conductivity or fertility, along with elevation, yield maps and vegetation indices (VIs) captured during the peak crop biomass period; (II) soil attributes in conjunction with VIs demonstrating strong correlations; (III) solely VIs exhibiting robust correlation with soil attributes and yield; (IV) VIs selected via random forests to identify the importance of the variable for estimating yield. The results showed that the VIs derived from satellite images could effectively replace other types of data. For plots where the natural spatial variability can be easily identified, all procedures favor obtaining MZ maps that allow reductions of 40% to 70% in yield variance, justifying their use. On the other hand, in plots with low natural spatial variability and that do not have reliable yield maps, different data sets used as input do not help in obtaining feasible MZ maps. For areas where anthropogenic activities with spatially differentiated treatment are already present, the exclusive use of VIs for the delineation of MZs must be carried out with reservations.

Impact of Land Cover Changes on Soil Type Mapping in Plain Areas: Evidence from Tongzhou District of Beijing, China

The flat terrain in the plain areas of Beijing, China makes the land easily accessible for cultivation and farming, providing vast opportunities for agricultural development. Meanwhile, these areas are also crucial for urban construction and economic growth. Soil type mapping plays a key role in understanding soil characteristics and guiding land management practices. However, accurately mapping soil types in plain regions can be challenging due to their low spatial variability and diverse land use types. Although land cover changes due to phenomena such as urbanization, agricultural expansion, and conversion of natural vegetation can significantly affect soil properties and distribution patterns, their impacts on soil type mapping remain unclear. This study investigated the impacts of land cover changes in plain areas on the accuracy of soil type mapping, hoping to provide effective assistance for soil type mapping in plain areas by analyzing their coupling relationship. Focusing on the 20 year land cover changes in Tongzhou District, this study utilizes a unified approach that combines expert knowledge, mixed sampling methods, and RF mapping techniques, while incorporating environmental covariates that have minimal period influence and synergistically using NDVI and land cover data from the same year. Transition matrices are used to reveal land cover changes, confusion matrices, and their derived indicators to analyze changes in soil type mapping accuracy, and coupling analysis is conducted between soil type change areas and land cover change areas. The results show that Tongzhou District has experienced rapid development over the past 20 years, with the area of construction land nearly doubling. Additionally, 29% of arable land has been converted into construction land, resulting in an increase in the accuracy of the soil map from 58.99% to 66.91% over the 20 year period. The soil type change area during this period accounts for 16.5% of the total area, with 51.9% of the changed areas overlapping with land cover change areas. These overlapping regions are predominantly influenced by human activities. In terms of cultivated land types in the study area, the quantity of arable land has decreased by approximately 29% over 20 years, while the proportion of Sandy loam calcareous fluvo-aquic soil and Light loam calcareous fluvo-aquic soil, which constitute nearly half of the soil type, has increased. These data demonstrate the coupling relationship between land cover changes and soil type variations. It is evident that improving the extent of land use in plain areas enhances the credibility of soil type mapping. Meanwhile, human activities impact land cover, which, in turn, affects and reflects changes in the soil type.

Improved Simultaneous Multi-slice imaging with Composition of k-space Interpolations (SMS-COOKIE) for myocardial T1 mapping.

The aim of this study is to develop and evaluate a regularized Simultaneous Multi-Slice (SMS) reconstruction method for improved Cardiac Magnetic Resonance Imaging (CMR). The proposed reconstruction method, SMS with COmpOsition of k-space IntErpolations (SMS-COOKIE) combines the advantages of Iterative Self-consistent Parallel Imaging Reconstruction (SPIRiT) and split slice-Generalized Autocalibrating Partially Parallel Acquisitions (GRAPPA), while allowing regularization for further noise reduction. The proposed SMS-COOKIE was implemented with and without regularization, and validated using a Saturation Pulse-Prepared Heart rate Independent inversion REcovery (SAPPHIRE) myocardial T1 mapping sequence. The performance of the proposed reconstruction method was compared to ReadOut (RO)-SENSE-GRAPPA and split slice-GRAPPA, on both retrospectively and prospectively three-fold SMS-accelerated data with an additional two-fold in-plane acceleration. All SMS reconstruction methods yielded similar T1 values compared to single band imaging. SMS-COOKIE showed lower spatial variability in myocardial T1 with significant improvement over RO-SENSE-GRAPPA and split slice-GRAPPA (P < 10-4). The proposed method with additional locally low rank (LLR) regularization reduced the spatial variability, again with significant improvement over RO-SENSE-GRAPPA and split slice-GRAPPA (P < 10-4). In conclusion, improved reconstruction quality was achieved with the proposed SMS-COOKIE, which also provided lower spatial variability with significant improvement over split slice-GRAPPA.

The impact of spatial variance on precision estimates in an auditory oddball paradigm

Predictive processing theories suggest that a principal function of the brain is to reduce the surprise of incoming sensory information by creating accurate and precise models of the environment. These models are commonly explored by looking at the prediction errors elicited when experience departs from predictions. One such prediction error is the mismatch negativity (MMN). Using this component, it is possible to examine the effect of external noise on the precision of the developed model. Recent studies have shown that the brain may not update its model every time there is a change in the environment, rather it will only update it when doing so will increase precision and or accuracy of the model. The current study examined this process using oddball sound sequences with high and low spatial variability and examining how this affected the elicited MMN to a duration deviant sound. The results showed a strong null effect of spatial variance both at a local and sequence levels. These results indicate that variability in the sound sequence will not invariably affect model precision estimates and thus the amplitude of the MMN component.

Spatial variability of throughfall in heavily fogged old-growth Fagus orientalis forests is controlled by fog precipitation and stand structural characteristics

Ecological impacts of the spatial heterogeneity of throughfall (TF) in forest ecosystems are recognized in ecohydrology. The objective of this study was to evaluate the spatial variability of TF and to quantify the effects of forest cover, fog, and gross rainfall (GR) characteristics on TF. Data were collected from May 2016 to November 2017 on an event basis in an intact, old-growth oriental beech (Fagus orientalis L.) stand in northern Iran. Over the measurement time period, 22 rainfall events were observed (total rain = 648 mm). Because the region experienced considerable fog, TF was greater than 100% of GR in 14 rainfall events with negative interception loss (total = -24.1 mm), causing low interception values, and 8 rainfall events were found with positive I (total = 25.5 mm). The low TF spatial variability and increasing TF depth observed in this study were related to increases in rainfall depth and intensity. TF was significantly and positively correlated with LAI and significantly and negatively correlated with canopy openness for rainfall events of more than 50 mm. The high amount of TF depth highlighted the role of LAI to enhance fog interception and the role of fog precipitation to increase TF in the study site. Spatial continuity at an effective range of 3-8 m was observed by variogram analyses. The increasing spatial continuity of TF with increasing GR observed in this study, along with the larger effective range for GR >50 mm and correspondingly similar LAI range, suggest an effect of LAI on the spatial patterning of TF for larger GR. Therefore, this heavily fogged ecosystem received considerable TF due to fog interception, and higher LAI increased TF for larger rainfall events. Our findings can optimize forest management practices by accounting for the effect of Fagus orientalis trees on the local hydrological processes of the Hyrcanian mountain forests.

Environmental heterogeneity modulates the effect of plant diversity on the spatial variability of grassland biomass

Plant productivity varies due to environmental heterogeneity, and theory suggests that plant diversity can reduce this variation. While there is strong evidence of diversity effects on temporal variability of productivity, whether this mechanism extends to variability across space remains elusive. Here we determine the relationship between plant diversity and spatial variability of productivity in 83 grasslands, and quantify the effect of experimentally increased spatial heterogeneity in environmental conditions on this relationship. We found that communities with higher plant species richness (alpha and gamma diversity) have lower spatial variability of productivity as reduced abundance of some species can be compensated for by increased abundance of other species. In contrast, high species dissimilarity among local communities (beta diversity) is positively associated with spatial variability of productivity, suggesting that changes in species composition can scale up to affect productivity. Experimentally increased spatial environmental heterogeneity weakens the effect of plant alpha and gamma diversity, and reveals that beta diversity can simultaneously decrease and increase spatial variability of productivity. Our findings unveil the generality of the diversity-stability theory across space, and suggest that reduced local diversity and biotic homogenization can affect the spatial reliability of key ecosystem functions.

Carbon emission and export from the Ket River, western Siberia

Abstract. Despite recent progress in the understanding of the carbon (C) cycle of Siberian permafrost-affected rivers, spatial and seasonal dynamics of C export and emission from medium-sized rivers (50 000–300 000 km2 watershed area) remain poorly known. Here we studied one of the largest tributaries of the Ob River, the Ket River (watershed = 94 000 km2), which drains through pristine taiga forest of the boreal zone in the West Siberian Lowland (WSL). We combined continuous and discrete measurements of carbon dioxide (CO2) concentration using submersible CO2 sensor and floating chamber flux (FCO2), with methane (CH4), dissolved organic and inorganic C (DOC and DIC, respectively), particulate organic C and total bacterial concentrations over an 800 km transect of the Ket River main stem and its 26 tributaries during spring flood (May 2019) and 12 tributaries during summer baseflow (end of August–beginning of September 2019). The partial pressure of CO2 (pCO2) was lower and less variable in the main stem (2000 to 2500 µatm) compared to that in the tributaries (2000 to 5000 µatm). In the tributaries, the pCO2 was 40 % higher during baseflow compared to spring flood, whereas in the main stem, it did not vary significantly across the seasons. The methane concentration in the main stem and tributaries was a factor of 300 to 1900 (flood period) and 100 to 150 times lower than that of CO2 and ranged from 0.05 to 2.0 µmol L−1. The FCO2 ranged from 0.4 to 2.4 g C m−2 d−1 in the main channel and from 0.5 to 5.0 g C m−2 d−1 in the tributaries, being highest during August in the tributaries and weakly dependent on the season in the main channel. During summer baseflow, the DOC aromaticity, bacterial number, and needleleaf forest coverage of the watershed positively affected CO2 concentrations and fluxes. We hypothesize that relatively low spatial and seasonal variability in FCO2 of the Ket River is due to a flat homogeneous landscape (bogs and taiga forest) that results in long water residence times and stable input of allochthonous dissolved organic matter (DOM), which dominate the FCO2. The open water period (May to October) C emission from the fluvial network (main stem and tributaries) of the Ket River was estimated to 127 ± 11 Gg C yr−1, which is lower than the downstream dissolved and particulate C export during the same period. The estimated fluvial C emissions are highly conservative and contain uncertainties linked to ignoring hotspots and hot moments of emissions, notably in the floodplain zone. This stresses the need to improve the temporal resolution of FCO2 and water coverage across seasons and emphasizes the important role of WSL rivers in the release of CO2 into the atmosphere.

Spatial and temporal variability of microplastic abundance in estuarine intertidal sediments: Implications for sampling frequency

Microplastics (<5 mm) are well documented across shorelines worldwide; however, high variability in microplastic abundance is often observed within and among field studies. The majority of microplastic surveys to date consist of single sampling events that do not consider spatiotemporal variability as a potential confounding factor in the interpretation of their results. Therefore, these surveys may not accurately capture or reflect levels of microplastic contamination in the environment. Here, we provide the first investigation of small-scale spatial and temporal variability of microplastic abundance, distribution, and composition in the intertidal zone of an urbanized US estuary to better understand the short-term, daily spatiotemporal variability of microplastics in dynamic coastal environments. Intertidal sediment was collected from both the low and high intertidal zones of a sandy estuarine beach located in South Carolina, southeastern US every 1 to 2 days at low tide over 17 days (12 sampling events; total n = 72). Study-wide, microplastic abundance ranged from 44 to 912 microplastics/m2 and consisted primarily of polyethylene, nylon, polyester, and tire (or tyre) wear particles. High temporal variability was observed, with microplastic abundance differing significantly among sampling events (p = 0.00025), as well as among some consecutive tidal cycles occurring within 12 h of each other (p = 0.007). By contrast, low spatial variability was observed throughout the study with no significant differences in microplastic abundance detected between the low and high intertidal zones (p = 0.76). Of the environmental factors investigated, wind direction on the day of sampling had the greatest effect on temporal microplastic variability. Our results demonstrate that there can be significant temporal variability of microplastic abundance in estuarine intertidal sediments and are important for informing the methods and interpretation of future microplastic surveys in dynamic coastal environments worldwide.

Utility of outdoor central site monitoring in assessing exposure of school children to ultrafine particles

Epidemiological studies investigating the association between daily particle exposure and health effects are frequently based on a single monitoring site located in an urban background. Using a central site in epidemiological time-series studies has been established based on the premises of low spatial variability of particles within the areas of interest and hence the adequacy of the central sites to monitor the exposure. This is true to a large extent in relation to larger particles (PM2.5, PM10) that are typically monitored and regulated. However, the distribution of ultrafine particles (UFP), which in cities predominantly originate from traffic, is heterogeneous. With increasing pressure to improve the epidemiology of UFP, an important question to ask is, whether central site monitoring is representative of community exposure to this size fraction of particulate matter; addressing this question is the aim of this paper. To achieve this aim, we measured personal exposure to UFP, expressed as particle number concentration (PNC), using Philips Aerasense Nanotracers (NT) carried by the participants of the study, and condensation particle counters (CPC) or scanning mobility particle sizers (SMPS) at central fixed-site monitoring stations. The measurements were conducted at three locations in Brisbane (Australia), Cassino (Italy) and Accra (Ghana). We then used paired t-tests to compare the average personal and average fixed-site PNC measured over the same 24-h, and hourly, periods. We found that, at all three locations, the 24-h average fixed-site PNC was no different to the personal PNC, when averaged over the study period and all the participants. However, the corresponding hourly averages were significantly different at certain times of the day. These were generally times spent commuting and during cooking and eating at home. Our analysis of the data obtained in Brisbane, showed that maximum personal exposure occurred in the home microenvironment during morning breakfast and evening dinner time. The main source of PNC for personal exposure was from the home-microenvironment. We conclude that the 24-h average PNC from the central-site can be used to estimate the 24-h average personal exposure for a community. However, the hourly average PNC from the central site cannot consistently be used to estimate hourly average personal exposure, mainly because they are affected by very different sources.

Protons: Critical Species for Resistive Switching in Interface‐Type Memristors

AbstractInterface‐type (IT) resistive switching (RS) memories are promising for next generation memory and computing technologies owing to the filament‐free switching, high on/off ratio, low power consumption, and low spatial variability. Although the switching mechanisms of memristors have been widely studied in filament‐type devices, they are largely unknown in IT memristors. In this work, using the simple Au/Nb:SrTiO3 (Nb:STO) as a model Schottky system, it is identified that protons from moisture are key element in determining the RS characteristics in IT memristors. The Au/Nb:STO devices show typical Schottky interface controlled current–voltage (I–V) curves with a large on/off ratio under ambient conditions. Surprisingly, in a controlled environment without protons/moisture, the large I–V hysteresis collapses with the disappearance of a high resistance state (HRS) and the Schottky barrier. Once the devices are re‐exposed to a humid environment, the typical large I–V hysteresis can be recovered within hours as the HRS and Schottky interface are restored. The RS mechanism in Au/Nb:STO is attributed to the Schottky barrier modulation by a proton assisted electron trapping and detrapping process. This work highlights the important role of protons/moisture in the RS properties of IT memristors and provides fundamental insight for switching mechanisms in metal oxides‐based memory devices.

Spatio‐temporal variation in deep soil water use patterns of overstorey and understorey layers in subtropical plantations predicts community assembly

Abstract Deep soil water utilization allows plants to cope with drought stress. However, little is known about the roles of the understorey layers in driving spatio‐temporal variations of deep soil water in forests and how the patterns of deep soil water use among life‐forms contribute to community assembly processes. We assessed the spatio‐temporal patterns and determinants of deep water utilization of tree, shrub and herb layers in subtropical coniferous plantations and investigated associations between deep water use parameters and dominance and richness of understorey vegetation. We found that the understorey layer had a higher reliance on deep soil water in the dry season, a larger seasonal plasticity of deep soil water uptake, but lower spatial variability in deep soil water utilization than the tree layer. We showed that greater reliance of the tree layer on deep soil water was associated with decreased shrub layer diversity, whereas greater reliance of the shrub layer on deep water was associated with increased herb layer diversity. Synthesis. Our results highlight the roles of understorey layers in driving the temporal dynamics of deep soil water in forests and improve our understanding of how deep soil water use patterns among life‐forms shape community assembly in forests.

Rapid restructuring of the odontocete community in an ocean warming hotspot.

Cetaceans are important consumers in marine ecosystems, but few studies have quantified their climate responses. The rapid, directional warming occurring in the Northeast United States (NEUS) provides a unique opportunity to assess climate impacts on cetaceans. We used stranding data to examine changes to the distribution and relative abundance of odontocetes from 1996 to 2020 in both the NEUS and the Southeast United States (SEUS), which is not warming. We conducted simulations to determine the number of stranding events needed to detect a distributional shift for each species given the speed of the shift and the spatial variability in strandings. We compared observed shifts to climate velocity. Smaller sample sizes were needed to detect more rapid poleward shifts, particularly for species with low spatial variability. Poleward shifts were observed in all species with sufficient sample sizes, and shifts were faster than predicted by climate velocity. For species whose trailing edge of distribution occurred in the NEUS, the center of distribution approached the northern limit of the NEUS and relative abundance declined through time, suggesting shifts north out of US waters. The relative abundance of warm water species in the stranding record increased significantly in the NEUS while that of cool water species declined significantly as their distributions shifted north out of the NEUS. Changes in the odontocete community were less apparent in the SEUS, highlighting the importance of regional warming. Observed poleward shifts and changes in species composition suggest a reorganization of the odontocete community in the NEUS in response to rapid warming. We suggest that strandings provide a key dataset for understanding climate impacts on cetaceans given limitations of survey effort and modeling approaches for predicting distributions under rapidly changing conditions. Our findings portend marked changes to the distribution of highly mobile consumer species across international boundaries under continued warming.

Dynamic regionalization of the Panama Bight, Eastern Tropical Pacific, using remote sensing data

ABSTRACT This study establishes the dynamic biogeographic regions (DBGRs) of the Panama Bight (PB) based on remote-sensing reflectance (Rrs412 and Rrs488) of the MODIS-Aqua sensor and the sea-surface temperature (SST) and the concentration of chlorophyll a (Chl-a) derived from the same sensor. Monthly images with a spatial resolution of 4 km from the MODIS-Aqua sensor were used for July 2002–July 2020. To define the DBGR, the first standardized empirical orthogonal function (SEOF1) was calculated. Due to the low spatial variability in the PB, the number of DBGRs found with the analysis of the SST and Chl-a was lower than that found in the analysis of the Rrs. Therefore, the DBGRs of the PB were delineated based only on the study of the Rrs. The PB was divided into 11 DBGRs belonging to two provinces: oceanic and coastal. Five of the DBGRs were coastal, the others oceanic, including a transition one. The coastal regions were characterized by the highest averages of Chl-a and high SST. On the other hand, the regions located northeast of the PB (Panama Gulf, PB-transition, and Choco) were characterized by an average SST greater than 28°C and an average Chl-a between 0.8 and 1.0 mg·m−3. The DBGRs located further west (west and PB-west) showed an average SST greater than 28°C and the lowest concentrations of Chl-a. The DBGRs PB-east and PB-central, presented average SSTs of 26.7–27.1°C and average Chl-a concentrations lower than 0.5 mg·m−3. The south Ecuador DBGR presented the lowest average SST (<25 °C) and average Chl-a of 0.50 mg·m−3. Our approach allowed us to generate a dynamic regionalization of the PB, which most of the year has small SST and Chl-a gradients, making this an alternative to the homogeneous areas generated from the thermohaline structure.